Gravity aided navigation using Viterbi map matching algorithm

TL;DR

This paper introduces a novel gravity-based navigation method that employs a Viterbi map matching algorithm within a hidden Markov model framework to improve vehicle positioning in GNSS-denied environments.

Contribution

It presents a new formulation of gravity map matching using HMM and a Viterbi algorithm, enhancing robustness and accuracy over existing methods.

Findings

Demonstrates improved accuracy in vehicle positioning using gravity map matching.

Shows robustness of the proposed Viterbi algorithm in realistic scenarios.

Validates the approach with a realistic gravity map.

Abstract

In GNSS-denied environments, aiding a vehicle's inertial navigation system (INS) is crucial to reducing the accumulated navigation drift caused by sensor errors (e.g. bias and noise). One potential solution is to use measurements of gravity as an aiding source. The measurements are matched to a geo-referenced map of Earth's gravity in order to estimate the vehicle's position. In this paper, we propose a novel formulation of the map matching problem using a hidden Markov model (HMM). Specifically, we treat the spatial cells of the map as the hidden states of the HMM and present a Viterbi style algorithm to estimate the most likely sequence of states, i.e. most likely sequence of vehicle positions, that results in the sequence of observed gravity measurements. Using a realistic gravity map, we demonstrate the accuracy of our Viterbi map matching algorithm in a navigation scenario and illustrate its robustness compared to existing methods.